Semiotics, Meaning, and Discursive Neural Networks

Knowledge representation is too complicated to be well-served by the traditional semantic
approach (e.g., Katz, 1972; Winston, 1977). The problems encountered by Rumelhart and
McClelland (1986), as pointed out by Fodor and Pylyshyn (1988), are endemic to the model
employed. Eco (1976, 1984) and others have urged that we see all systematically-generated
information as parts of a "science of signs", semiotics. The three-faceted semiotic structure
embraces information from the genetic level to language — and production. Semiotics fits
modern neurolinguistic approaches (e.g., Dingwall, 1980) and neurophysiological theorizing
(Arnold, 1984; Black et al, 1988; Posner & Keele, 1968). It also enables us to employ
"holographic models" (Pribram, 1971) — and to extend our models to higher level processes
(Leven, 1987a, 1987b; Levine, 1986). Ultimately, both internal and interpersonal interactions are
discursive.